Dynamically Derived Tropical Cyclone Intensity Changes over the Western North Pacific

1JANUARY 2012 W U A N D Z H A O 89 Dynamically Derived Tropical Cyclone Intensity Changes over the Western North Pacific LIGUANG WU AND HAIKUN ZHAO Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China (Manuscript received 14 October 2010, in final form 14 June 2011) ABSTRACT The study of the impact of global warming on tropical cyclone (TC) intensity is subject to uncertainty in historical datasets, especially in the western North Pacific (WNP) basin, where conflicting results have been found with the TC datasets archived in different organizations. In this study the basinwide TC intensity in the WNP basin is derived dynamically with a TC intensity model, based on the track data from the Joint Typhoon Warning Center (JTWC), the Regional Specialized Meteorological Center (RSMC) of Tokyo, and the Shanghai Typhoon Institute (STI) of the China Meteorological Administration. The dynamically derived TC intensity is compared to the three datasets and used to investigate trends in TC intensity. The associated contributions of changes in SST, vertical wind shear, and prevailing tracks are also examined. The evolution of the basinwide TC intensity in the JTWC best-track dataset can be generally reproduced over the period 1975 2007. Dynamically derived data based on the JTWC, RSMC, and STI track datasets all show an increasing trend in the peak intensity and frequency of intense typhoons, mainly because of the combined effect of changes in SST and vertical wind shear. This study suggests that the increasing intensity trend in the JTWC dataset is real, but that it may be overestimated. In contrast, the TC intensity trends in the RSMC and STI intensity datasets are dynamically inconsistent. Numerical simulations also suggest that the frequency of intense typhoons is more sensitive to changes in SST and vertical wind shear than the peak and average intensities defined in previous studies. 1. Introduction The response of tropical cyclone (TC) activity to global warming remains controversial, in part because of uncertainty in historical TC records (e.g., Chan 2006; Landsea et al. 2006; Landsea 2007; Kossin et al. 2007; Vecchi and Knutson 2008; Emanuel 2008). In the western North Pacific (WNP) basin, upward trends were found over the past three decades in the proportion of intense typhoons with a maximum wind speed larger than 59 m s 21 (Webster et al. 2005); the annual accumulated power dissipation index (PDI) that is a collective effect of the intensity, lifetime, and annual frequency of TCs (Emanuel 2005); and the average intensity that is obtained by averaging maximum wind speeds first over the lifetime for each TC and then for all of the TCs each year Corresponding author address: Dr. Liguang Wu, Pacific Typhoon Research Center, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China. E-mail: liguang@nuist.edu.cn in the basin (Wu et al. 2008). However, these trends were found solely in the Joint Typhoon Warning Center (JTWC) best-track dataset. On average, 27 TCs occur in the WNP basin, which account for 33% of the total TCs in the world (Chan 2005). In addition to the JTWC, several other organizations also maintain their own historical TC records. Although their differences in TC tracks are generally small, in particular after the advent of satellite monitoring in the 1960s (Emanuel 2008; Song et al. 2010), the intensity change derived from these datasets was quite different from that in the JTWC dataset (Wu et al. 2006; Song et al. 2010). Recently, TC intensity change in the JTWC dataset was compared with that in the datasets from the Regional Specialized Meteorological Center (RSMC) of Tokyo (Japan), the Hong Kong Observatory (HKO) of China, and the Shanghai Typhoon Institute (STI) of the China Meteorological Administration (Wu et al. 2006; Kamahori et al. 2006; Yu et al. 2007; Song et al. 2010). It is found that the upward trends reported in the aforementioned studies were not detected except in the JTWC dataset. DOI: 10.1175/2011JCLI4139.1 Ó 2012 American Meteorological Society

90 J O U R N A L O F C L I M A T E VOLUME 25 The different trends of the TC activity in the WNP basin may be due to differences in observing systems, reporting policies, and analysis methods. In addition, the temporal consistency of a TC dataset is particularly important for detecting climate trends in TC activity. To address the issue, Kossin et al. (2007) constructed a homogeneous dataset of TC intensity by applying a new objective algorithm to the satellite data from 1983 to 2005. They suggested that the previously documented trends in the WNP basin may be inflated or spurious. It is clear that the uncertainty involved in TC datasets has become an important issue in understanding the possible influence of global warming on TC activity in the WNP basin. As mentioned above, several relevant studies have been focused on the intensity difference in the TC datasets (Wu et al. 2006; Kamahori et al. 2006; Yu et al. 2007; Elsner et al. 2008; Song et al. 2010), but the relative reliability of TC datasets was not reasonably addressed because of the lack of direct observations on TC intensity. The issue becomes more complicated because different average times are used for estimating TC maximum wind speeds from one center to another. While the RSMC intensity was a 10-min average wind speed, JTWC and STI used 1- and 2-min average wind speeds to estimate TC intensity, respectively. In this study, using the approach proposed by Emanuel (2006), we assess the reliability of the TC intensity estimates in the JTWC, RSMC, and STI TC datasets, and the associated changes over the period 1975 2007. In Emanuel (2006), the synthetic TCs are produced with the formation, motion, and intensity models. After successfully reproduced the observed TC climatology in the period 1980 2006, Emanuel et al. (2008) applied this approach to quantify the possible influence of global warming on TC activity, suggesting that global warming should reduce the global frequency of hurricanes, though their intensity may increase in some locations. In this approach, the wind field of each storm is predicted using a deterministic, coupled air sea model, the Coupled Hurricane Intensity Prediction System (CHIPS). The intensity model uses a parameterization of the deleterious effect of environmental wind shear on TCs. In the present study, we integrate the CHIPS model along the observed tracks in the historical TC datasets to obtain TC intensity data. Note that the dynamically derived TC intensity is independent of historical TC intensity estimates in the JTWC, RSMC, and STI datasets. 2. Data and indices for measuring basinwide TC intensity Three TC best-track datasets from JTWC, RSMC, and STI include the information of TC activity in the WNP basin, such as the TC center position (latitude and longitude), the maximum sustained wind speed, and the minimum sea level pressure at least for each 6-h interval. The three datasets have been used in previous studies on TC climate change (e.g., Wu et al. 2006; Kamahori et al. 2006; Yu et al. 2007; Song et al. 2010). Despite the different average times used to estimate TC maximum wind speeds, a TC in this study is selected if its maximum wind speed in these datasets exceeds 17.2 m s 21. Although our analysis period ranges from 1951 to 2007, our main focus is placed on the period 1975 2007 because we assume that the TC frequency and track data during this period were relatively reliable in the WNP basin (Emanuel 2008). We used Takahashi s wind pressure relationship to calculate the sustained maximum wind speeds for the period 1951 1976 because the RSMC sustained maximum winds first became available in 1977 (Emanuel 2005). If p c is the reported central pressure (hpa) and V is the maximum wind speed (m s 21 ), then the wind pressure p relationship can be written as V 5 5:95 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (1010 2 p c ).To quantify the vertical shear of environmental winds, the monthly-mean wind fields are obtained from the National Centers for Environmental Prediction National Center for Atmospheric Research (NCEP NCAR) reanalysis dataset with a horizontal resolution of 2.58 latitude 3 2.58 longitude. The monthly National Oceanic and Atmospheric Administration (NOAA) extended reconstructed SST dataset (ERSST version 3) is used in this study. The focus of this study is on the basinwide TC intensity changes in the WNP basin. Based on previous studies (Webster et al. 2005; Wu 2007; Wu et al. 2008), three indices are defined in this study to measure the basinwide annual TC intensity: average intensity, peak intensity, and frequency of intense typhoons (categories 4 and 5). Here, the annual peak intensity is obtained by averaging the peak intensities of all of the TCs each year. The statistical significance of linear trends in this study is tested with the Mann Kendall method (Kundzewicz and Robson 2000). 3. Dynamically derived climate changes in the basinwide TC intensity The TC intensity model adopted from Emanuel et al. (2008) is an axisymmetric numerical atmospheric model, coupled with a simple one-dimensional ocean model. In this study, all of the observed TCs are allowed to move along the observed tracks, and their intensity evolution is simulated with the intensity model. The observed TC tracks, SST, and vertical wind shear are required as the model input. The vertical wind shear is calculated as the difference of monthly wind speeds between 850 and

1JANUARY 2012 W U A N D Z H A O 91 200 hpa (Wu 2007). The intensity model is initialized with a warm-core cyclonic vortex. After a series of numerical experiments, the maximum wind speed of the initial vortex is set to be 21 m s 21 because the model vortex weakens at the beginning of the simulation. A few sensitivity experiments are conducted with different maximum wind speeds of the initial vortex, indicating that the simulated spatial patterns and temporal variations are very similar except for the magnitude of TC intensity. The other parameters of the initial vortex are the same as those in Emanuel et al. (2008). The environmental relative humidity in the boundary layer and in the middle troposphere are held constant 1 (80% and 45%, respectively), and constant outflow temperature (2708C) is used in this study. In addition, the influence of SST cooling associated with TC ocean included is not included. The same model setup is used for the three TC datasets. The derived basinwide average intensity, peak intensity, and annual frequency of intense typhoons, which are based on 1496 TCs in the JTWC dataset for the period 1951 2007, are shown in Fig. 1. In comparison with the observed intensity indices in the JTWC dataset, the model can reasonably reproduce the evolution of the basinwide TC intensity from 1965 to 2007. The correlation coefficients between the observed and simulated time series over this period are 0.53, 0.72, and 0.70 for the average intensity, peak intensity, and annual frequency of intense typhoons, respectively, significant at the 95% level. Close inspection indicates that the intensity model will simulate the interannual variations of TC intensity during the period. However, the simulated intensity indices are very different from those observed in the JTWC dataset prior to the mid-1960s, which was coincident with presatellite years (Knutson et al. 2010). The large difference may be due to intensity uncertainty in the JTWC dataset and/or inaccurate information of TC tracks prior to presatellite years. Figure 1 suggests that the intensity estimates in the JTWC dataset are generally consistent with the dynamically derived intensity after the mid-1960s. In contrast, the model-derived intensity is very different from the intensity estimates in the RSMC and STI datasets, in particular after 1975. The RSMC and STI datasets recorded 1515 and 1438 TCs, respectively. Figure2showsacomparisonofthethreeintensity 1 Numerical experiments are also conducted with the environmental relative humidity in the boundary layer and in the middle troposphere from the NCEP NCAR reanalysis dataset, but the correlations of the simulated intensity indices with those in the JTWC dataset are reduced. FIG. 1. Simulated (open dots) time series of (a) the basinwide average intensity (m s 21 ), (b) the basinwide peak intensity (m s 21 ), and (c) the annual frequency of intense typhoons in comparison with the corresponding observations (closed dots) in the JTWC dataset during the period 1951 2007. indices between the RSMC dataset and the corresponding simulations. Although the maximum wind speeds are calculated from the minimum central pressure prior to 1971, the remarkable difference between the simulation and observation occurs after the mid-1970s. In general, the simulated average intensity, peak intensity, and frequency of intense TCs are much larger than those derived from the observation after the mid-1970s, and their differences between the observed and the simulated seem to increase with time, especially in the frequency of intense typhoons. Emanuel (2008) noted that the PDI calculated

92 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 2. As in Fig. 1, but for the RSMC dataset. FIG. 3. As in Fig. 1, but for the STI dataset. from the RSMC dataset is well correlated with SST prior to the cessation of aircraft. Figure 2 suggests that the RSMC intensity estimates are more dynamically consistent prior to 1975. For the STI dataset (Fig. 3), like the RSMC dataset, the simulated intensity indices are generally larger than the observed after the mid-1970s; however, a decreasing trend can be found in the average and peak intensities prior to the mid-1970s, which is also found in the JTWC observation (Fig. 1). Based on the simulated intensity data with the JTWC, RSMC, and STI datasets, the intensity trends are examined over the period 1975 2007. As shown in Table 1, although the observed trends are different from one dataset to another, the intensity indices simulated with the three datasets indicate an increasing trend in TC intensity in the WNP basin from 1975 to 2007. These trends are not statistically significant at the 95% level. The observed significant decreasing trends in the frequency of intense typhoons in the RSMC and STI datasets cannot be reproduced with the intensity model, while the simulated trends in the average intensity and the frequency of intense typhoons with the JTWC dataset are much smaller than the observed ones. The increasing trends simulated with the JTWC dataset are in good agreement with Kossin et al. (2007). They conducted a 23-yr reanalysis of TC intensity and suggested that a systematic overestimation of intensity was found during the period 1988 2004 in the JTWC dataset. Note

1JANUARY 2012 W U A N D Z H A O 93 TABLE 1. Comparisons of the observed and simulated trends [(m s 21 )yr 21 ] in average intensity, peak intensity, and frequency of intense typhoons (categories 4 and 5) during the period 1975 2007. Trends above the 95% confidence level are in bold. Average intensity Peak intensity Intense typhoons Dataset Observation Simulation Observation Simulation Observation Simulation JTWC 0.120 0.008 0.108 0.023 0.143 0.073 RMSC 0.001 0.090 20.066 0.124 20.060 0.087 STI 20.030 0.018 20.068 0.056 20.091 0.044 that the similar trends simulated with the different datasets are due to the small differences in their tracks after the satellite data became available since the 1960s (Song et al. 2010). Emanuel (2005) found that the annual accumulated PDI has increased markedly in the WNP basin since the mid-1970s and attributed the upward trend to both longer storm lifetimes and greater storm intensities. Wu et al. (2008) argued that the upward trend of the PDI over the period 1975 2004 was not statistically significant although the average intensity increased by 11%, which was significant at the 95% level. Considering the reduced upward trend in average intensity, we compare the simulated PDI with the observation. As shown in Fig. 4a, the simulated PDI with the JTWC dataset agrees well with the observed over the period 1975 2007. The correlation reaches 0.85. In agreement with Wu et al. (2008), no significant trend can be found in the time series of the simulated or observed PDI. Figure 4b further shows the time series of the PDI simulated with the RSMC and STI datasets, compared to the simulation with the JTWC dataset. The time series of the PDI simulated with the RMSC and STI datasets are well correlated with the one simulated with the JTWC dataset (their correlation coefficients exceed 0.9). To examine the spatial distribution of the intensity trend in the JTWC dataset, we first compare the spatial distribution of TC intensity simulated with the JTWC dataset with the corresponding observation (Fig. 5). The TC intensity for each 2.58 latitude 3 2.58 longitude box is calculated by averaging the maximum wind speeds of those TCs that entered the box during the period 1965 2007. As shown in Fig. 5, the model can reasonably reproduce the overall spatial pattern of the TC intensity, including the strong intensity area to the east of the Taiwan island. Figure 6 further shows the comparison of the spatial distribution of the intensity change between the simulation and observation over the period 1975 2007, which is also based on the JTWC dataset. In the observation (Fig. 6a), positive trends were dominant in the whole basin except the decreasing trends to the southeast of Philippines. It is likely that strong upward trends that are found north of 208N or east of 1408E may lead to the observed significant trend in average intensity and the frequency of intense typhoons (Table 1). Compared to the JTWC observation, the area and magnitude of the simulated upward trends are reduced, occurring mainly in the vicinity of Taiwan, to the south of Japan, and over the central northern Pacific, whereas the downward trends can be found in the South China Sea and over the ocean from southeast of the Philippines to south of Japan (Fig. 6b). In summary, the TC intensity model can reasonably reproduce the basinwide evolution of TC intensity in the JTWC dataset, while the intensity changes calculated FIG. 4. (a) Comparison of the time series of the simulated (open dots) and observed (closed dots) annual accumulated PDI [(10 7 m 3 s 23 )yr 21 ] based on the JTWC dataset, and (b) time series of simulated annual accumulated PDI based on RSMC (open squares) and STI (closed dots) dataset, compared to the one simulated with the JTWC dataset (open dots).

94 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 5. Spatial distribution of the (a) observed and (b) simulated intensity over the period 1965 2007 based on the JTWC best-track dataset with contour intervals of 2.5 m s 21. with the RMSC and STI intensity data over the period 1975 2007 are not dynamically consistent with the simulations based on the corresponding datasets. The numerical simulations based on the JTWC, RMSC, and STI track data suggest a much smaller upward trend in the TC intensity than that derived from the JTWC intensity data. All of the simulated trends in TC intensity are statistically insignificant at the 95% level. 4. Contributions of changes in SST and vertical wind shear The influences of SST and vertical wind shear on the climate change of TC intensity were discussed in many previous studies (e.g., Goldenberg et al. 2001; Emanuel 1987, 2005, 2008; Webster et al. 2005; Wu et al. 2008). In addition to the control run (CTRL), in this study three more experiments are conducted based on the JTWC track data to examine the individual contributions of changes in SST and vertical wind shear to the basinwide intensity (Table 2). Experiment S75 (V75) is the same as the CTRL experiment but with the SST (vertical wind shear) being set to be fixed in 1975, while experiment FIG. 6. Spatial distribution of the linear trends [(m s 21 )yr 21 ] in tropical cyclone intensity over the period 1975 2007 based on the JTWC best-track dataset from (a) observation, (b) CTRL, and (c) VT75. VT75 is run with both of the SST and vertical wind shear that are set to be fixed in 1975. The influence of SST (shear) changes on TC intensity can be examined by contrasting V75 (S75) with VT75. In addition, the combined effect of SST and shear changes can be examined by contrasting VT75 with CTRL. Figure 7a shows the linear trends of vertical wind shear in the peak typhoon season (July September) over the period 1975 2007. The significant decrease of vertical wind shear occurred in the two regions. One region extended southwest, mainly from Japan to Taiwan, and the other region is over the tropical WNP south of 108N, while the significant increase in vertical wind shear occurred in the central Pacific. Table 3 indicates that the shear effect enhanced TC intensity in terms of the average intensity, peak intensity, frequency of intense typhoons, and PDI; however, the enhancement was not statistically significant over the period 1975 2007.

1JANUARY 2012 W U A N D Z H A O 95 TABLE 2. Summary of numerical experiments conducted with the tropical cyclone intensity model. Experiments CTRL T75 V75 VT75 Simulation description Observed SST and vertical wind shear are used from 1975 to 2007. SST is fixed in 1975, but observed vertical wind shear is used from 1975 to 2007. Vertical wind shear is fixed in 1975, but observed SST is used from 1975 to 2007. Both SST and vertical wind shear are fixed in 1975. The linear trends of SST over the period 1975 2007 are shown in Fig. 7b, which were dominated with the increasing trends in the whole WNP basin. The upward trends range from 0.15 to 0.258C decade 21 south of 308N, with the maximum warming of 0.258C decade 21 in the subtropical region around 1558E. As indicated in Table 3, the increasing SST all over the WNP basin led to increases in the average intensity, peak intensity, frequency of intense typhoons, and PDI. The effect of SST warming on TC intensity is larger than that of decreasing vertical shear over the period 1975 2007. The SST effect on the frequency of intense typhoons was statistically significant at the 95% level. It is interesting to note that the combined effect of SST and vertical shear changes, which is calculated based on the difference between CTRL and VT75, is much larger than the sum of their individual contributions, in particular for the peak intensity and frequency of intense typhoons. The individual contribution of SST (shear) change is derived from the difference between V75 (T75) and VT75. The combined effect on intensity increases by 53% and 33%, CTRL and VT75 respectively, compared to the sums of their individual contributions to the peak intensity and frequency of intense typhoons. As a result, the combined effect on the peak intensity and frequency of intense typhoons are statistically significant at the 95% level. 5. Contribution of changes in prevailing typhoon tracks Wu et al. (2005) found that typhoon prevailing tracks changed systematically over the period 1965 2004, and Wu and Wang (2008) argued that shifts in the TC prevailing tracks may have allowed more storms to follow a longer journey that favors the development of intense typhoons. In this study, the effect of prevailing track changes is examined by comparing the trends in VT75 with the ones in CTRL. Following Holland (1983), the large-scale steering flow is defined in this study as the mean flow from 850 to FIG. 7. Observed linear trends in (a) vertical wind shear and (b) SST for the peak typhoon season (July September) over the period 1975 2007. Shaded grids pass the significant test at the 95% level. Contour intervals are (a) 0.5 (m s 21 ) decade 21 and (b) 0.058C decade 21. 300 hpa. Figure 8a shows the resulting linear trends of the large-scale steering flow, which is characterized by a cyclonic circulation centered over eastern China, with westerly anomalies over the South China Sea and WNP south of 308N. In comparison, Fig. 9b shows the corresponding changes of the frequency of occurrence during this period. The frequency of occurrence indicates how many TCs enter a specific grid box of 2.58 latitude 3 2.58 longitude. The higher the frequency in a given box, the more TCs affect the box. As suggested by Wu et al. TABLE 3. Contributions of changes in vertical wind shear, SST, and tropical cyclone track to the simulated trends [(m s 21 )yr 21 for intensity, 10 5 (m 3 s 23 )yr 21 for PDI] in average intensity, peak intensity, frequency of intense typhoons (categories 4 and 5), and PDI based on the JTWC best-track dataset during the period 1975 2007. Trends above the 95% confidence level are in bold. Effects Average Peak Categories 4 and 5 PDI Shear 0.008 0.004 0.016 0.281 SST 0.015 0.027 0.035 0.548 Shear and SST 0.024 0.049 0.068 0.834 Track 20.016 20.026 0.006 0.143

96 J O U R N A L O F C L I M A T E VOLUME 25 FIG. 8. (a) Observed trends in large-scale steering flows in the peak typhoon season over the period 1975 2007, and (b) observed trends in the frequency of occurrence over the period 1975 2007. Contour intervals are 0.15 decade 21 in (b). Shaded grids pass the significant test at the 95% level. Arrows in (b) show the climatologic typhoon prevailing tracks from Wu et al. (2005). (2005), TCs over in the WNP take three prevailing tracks, as shown in Fig. 8b. The first (track 1) is a westwardmoving track from the tropical western North Pacific to the South China Sea. TCs that affect the coastal region of East Asia take the second prevailing track (track 2), while some typhoons tend to recurve northeastward east of 1308E (track 3), usually during WNP subtropical ridge splits. Figure 8 suggests that the westerly anomalies in large steering flows over the South China Sea andwnpsouthof308n prevented TCs from taking prevailing track 1, leading more TCs to taking prevailing track 2. Meanwhile, prevailing track 3 shifted westward over the period 1975 2007. The change of track 2 is similar to the influence of El Niño Modoki, (Ashok et al. 2007; Chen and Tam 2010), which has been reported more frequently in the recent decade (Yeh et al. 2009). Further investigation is needed to understand its possible association with the long-term change of track 2. FIG. 9. Time series of the (a) annual frequency and (b) mean formation longitude of TCs that formed in the WNP and entered the South China Sea over the period 1965 2007. Dashed lines indicate linear trends. The spatial distribution of the trends simulated in VT75 is shown in Fig. 6c. Comparing with the simulated trends in CTL (Fig. 6b), we can find that the effect of the prevailing track change can nearly account for the spatial distribution of the total intensity change, in part because of the relatively uniform contribution of SST change, in part because of the relatively small contribution of shear change. One region of upward trends is located off the coast of China mainland, extending from Japan to the northeast of the South China Sea. The other region is over the WNP between 1408 and 1508E. The downward trends can be found in the South China Sea and over the ocean from southeast of the Philippines to south of Japan (Fig. 6c). Because of the alternating increasing and decreasing pattern, the influence of the change in the prevailing track leads to insignificant changes in terms of the average intensity, peak intensity, frequency of intense typhoons, and PDI, as shown in Table 3. It is interesting to understand why the track shift leads to the decrease in intensity over the South China Sea. Figure 9 shows a decreasing trend in the number of TCs that entered into the South China Sea over the period 1965 2007 and a westward trend in the mean formation

1JANUARY 2012 W U A N D Z H A O 97 longitude for the TCs; that is to say, it is more likely for TCs with a more westward formation longitude to enter the South China Sea because of the westerly anomalies in the large-scale steering flow. As a result, the TCs that entered the South China Sea took an increasingly short journey with a decreasing time for intensification. 6. Summary In the WNP basin, the trend of TC intensity change over the past three decades or so remains controversial, in part because of uncertainty in historical datasets. The uncertainty involved in these datasets has become an important issue in understanding the possible influence of global warming on TC activity in the WNP basin. To address the issue, the basinwide TC intensity in the WNP basin is derived dynamically with the TC intensity model (Emanuel 2006), based on the track data in the JTWC, RSMC, and STI datasets. The TC intensity model can reasonably reproduced the evolution of the basinwide TC intensity in the JTWC dataset, while intensity changes in the RMSC and STI intensity data are not consistent with the dynamically derived ones over the period 1975 2007. Our numerical simulations based on the JTWC, RMSC, and STI track data all show an increasing trend in TC intensity, suggesting that the increasing trend in the JTWC dataset is real, although the simulated trend is much smaller than the observed and statistically insignificant. This study agrees with Kossin et al. (2007) that the change of the TC intensity in the WNP basin may be overestimated in the JTWC dataset. The individual contributions of changes in SST, vertical wind shear, and prevailing tracks are investigated with the simulated intensity data based on the JTWC dataset. While the individual changes in vertical shear and prevailing tracks played an insignificant role in TC intensity, this study suggests that the warming SST over the period 1975 2007 significantly contributed to the activity of intense typhoons. Moreover, numerical simulations conducted in this study indicate that the combined effect of SST and vertical shear significantly enhanced the peak intensity and frequency of intense typhoons over the period 1975 2007. This study also suggests that the frequency of intense typhoons is a most sensitive index in response to changes in vertical wind shear and SST. Acknowledgments. The authors thank Prof. Kerry Emanuel for allowing us to use his TC intensity model. This research was jointly supported by the Social Commonwealth Research Program of the Ministry of Science and Technology of China (Grant GYHY200806009), the National Natural Science Foundation of China (Grant 40875038), the National Basic Research Program (Grant 2009CB421503) of China, and the graduate study innovation plan for universities in Jiangsu Province (Grant CX09B_224Z). REFERENCES Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006jc003798. Chan, J. C. L., 2005: Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific. Meteor. Atmos. Phys., 89, 143 152., 2006: Comment on Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 311, 1713. Chen, G., and C.-Y. Tam, 2010: Different impacts of two kinds of Pacific Ocean warming on tropical cyclone frequency over the western North Pacific. Geophys. Res. Lett., 37, L01803, doi:10.1029/2009gl041708. Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455, 92 95. Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483 485., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686 688., 2006: Climate and tropical cyclone activity: A new model downscaling approach. J. Climate, 19, 4797 4802., 2008: The hurricane climate connection. Bull. Amer. Meteor. Soc., 89, ES10 ES20., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347 367. Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nunez, and W. M. Gray, 2001: The recent increase in Atlantic hurricane activity: Causes and implications. Science, 293, 474 478. Holland, G. J., 1983: Tropical cyclone motion: Environmental interaction plus a beta effect. J. Atmos. Sci., 40, 338 342. Kamahori, H. N., N. Yamazaki, N. Mannoji, and K. Takahashi, 2006: Variability in intense tropical cyclone days in the western North Pacific. SOLA, 2, 104 107, doi:10.2151/sola.2006-027. Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157 163. Kossin,J.P.,K.R.Knapp,D.J.Vimont,R.J.Murnane,and B. A. Harper, 2007: A globally consistent reanalysis of hurricane variability and trends. Geophys. Res. Lett., 34, L04815, doi:10.1029/2006gl028836. Kundzewicz, Z. W., and A. Robson, Eds., 2000: Detecting trend and other changes in hydrological data. WCDMP Rep. WCDMP-45, WMO Tech. Doc. WMO/TD 1013, 157 pp. Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. Eos, Trans. Amer. Geophys. Union, 88, 197 202., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452 454. Song, J.-J., Y. Wang, and L. Wu, 2010: Trend discrepancies among three best track data sets of western North Pacific tropical cyclones. J. Geophys. Res., 115, D12128, doi:10.1029/ 2009JD013058. Vecchi, G. A., and T. R. Knutson, 2008: On estimates of historical North Atlantic tropical cyclone activity. J. Climate, 21, 3580 3600.

98 J O U R N A L O F C L I M A T E VOLUME 25 Webster, P. J., G. J. Holland, J. A. Curry, and H. R. Chang, 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844 1846. Wu, L., 2007: Impact of Saharan air layer on hurricane peak intensity. Geophys. Res. Lett., 34, L09802, doi:10.1029/2007gl029564., and B. Wang, 2008: What has changed the proposition of intense hurricanes in the last 30 years? J. Climate, 21, 1432 1439.,, and S. Geng, 2005: Growing typhoon influence on East Asia. Geophys. Res. Lett., 32, L18703, doi:10.1029/ 2005GL022937.,, and S. A. Braun, 2008: Implications of tropical cyclone power dissipation index. Int. J. Climatol., 28, 727 731. Wu, M.-C., K.-H. Yeung, and W.-L. Chang, 2006: Trends in western North Pacific tropical cyclone intensity. Eos, Trans. Amer. Geophys. Union, 87, 537 538. Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511 514. Yu, H., C. Hu, and L. Jiang, 2007: Comparison of three tropical cyclone intensity datasets. Acta Meteor. Sin., 21, 121 128.